Completed Sql3

[DhruvParashar246]

No description provided.

[super30admin]

Let me evaluate each solution one by one:

1. Consecutive Numbers.sql:

   • Correctness: The solution correctly identifies consecutive numbers by joining the Logs table with itself and checking for consecutive IDs and matching numbers. This is a standard approach for this type of problem.
   • Time Complexity: O(n^3) due to the triple self-join, which could be inefficient for large tables.
   • Space Complexity: O(n) for storing intermediate results.
   • Code Quality: The code is clear and well-formatted. Using DISTINCT is good to avoid duplicates.
   • Improvement: Could consider using window functions (LEAD/LAG) for better performance with large datasets.

2. Dynamic Pivoting of a Table.sql:

   • Correctness: The solution correctly implements dynamic pivoting using prepared statements and GROUP_CONCAT.
   • Time Complexity: O(n) for building the dynamic SQL, plus the execution time of the pivoted query.
   • Space Complexity: O(n) for storing the dynamic SQL string.
   • Code Quality: Well-structured with comments. Good use of GROUP_CONCAT_MAX_LEN.
   • Improvement: Could add error handling for the prepared statement.

3. Number of Passengers in Each Bus.sql:

   • Correctness: The solution correctly counts passengers per bus using a CTE to match passengers to the earliest arriving bus.
   • Time Complexity: O(n log n) for the join and grouping operations.
   • Space Complexity: O(n) for storing the CTE results.
   • Code Quality: Good use of CTE and clear logic. The commented line should be removed.
   • Improvement: Could consider adding an index on arrival_time for better performance.

4. User Activity.sql:

   • Correctness: The solution correctly counts active users per day within the specified date range.
   • Time Complexity: O(n) for the table scan and grouping.
   • Space Complexity: O(n) for storing the grouped results.
   • Code Quality: Simple and clear. The date range condition could be more explicit (e.g., using BETWEEN).
   • Improvement: Could add a comment explaining the date range selection.

General strengths:

• All solutions follow SQL best practices
• Proper use of aliases and formatting
• Correct handling of edge cases (like using LEFT JOIN in the bus problem)

Areas for improvement:

• Could add more comments explaining the logic
• Consider performance optimizations for larger datasets
• Remove commented debugging code in production solutions